Korean immigrants' social practice of heritage language acquisition and maintenance through technology
Bibliographic record
Abstract
Studying issues of heritage language (HL) maintenance is gaining more significance than ever as our lives become significantly more complex and dynamic because of frequent migration and the transnational diasporas that such migration creates in its wake. HL maintenance is important in multicultural environments because familial relationships depend heavily on successful communication among family members. Viewing HL maintenance as a social practice, this exploratory qualitative study attempts to understand how participants are involved in their children’s HL maintenance by investigating, comparing, and contrasting the participants’ attitudes and practices. This study recruited eight Korean immigrant families with different lengths of residence in Greater Vancouver, an area that has seen a steady growth in the numbers of Korean immigrants. Combining social practice theory and qualitative research, this study uses discourse analysis to explore the participants’ language ideologies and beliefs about HL maintenance. This study also explored actual parental involvement in their children’s HL acquisition and maintenance. Furthermore, this study examined participants’ technology use as a means of HL acquisition and maintenance. In particular, the participants’ online conversations were examined to explore language use. This study supports the view that the parental role is important, even paramount, in children’s HL maintenance, but goes beyond this to show how technology can play a positive role in HL acquisition and maintenance. There are three central findings. First, a match between parental attitudes and behaviours concerning HL acquisition and maintenance and contributes to their children’s HL maintenance. Second, a mismatch or inconsistency between parental attitudes and behaviours correlates with children’s HL attrition or loss. Third, language revitalization can occur through HL and cultural practices in various online activities such as synchronous and asynchronous online communication, including access to Korean websites and playing games in Korean. To conclude, examining HL maintenance as a social practice offers new insights into the complexity and dynamics of the social practices of HL maintenance in the lives of Korean immigrants in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".